Deploy BitNet-Transformer Trainer

BitNet_Transformer_Training.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AI Trading: BitNet-Transformer Training\n",
+    "This notebook trains a 25M parameter ternary-quantized Transformer on 10 years of market data.\n",
+    "\n",
+    "## 1. Setup Environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clone the repository\n",
+    "!git clone https://github.com/luohoa97/ai-trading.git\n",
+    "%cd ai-trading\n",
+    "\n",
+    "# Install dependencies\n",
+    "!pip install torch safetensors huggingface_hub pandas numpy yfinance scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Configuration\n",
+    "Set your Hugging Face credentials to upload the model after training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from google.colab import userdata\n",
+    "\n",
+    "# Best practice: Use Colab Secrets (the key icon on the left)\n",
+    "try:\n",
+    "    os.environ[\"HF_TOKEN\"] = userdata.get('HF_TOKEN')\n",
+    "    os.environ[\"HF_REPO_ID\"] = \"luohoa97/BitFin\"\n",
+    "    print(\"✅ HF credentials loaded from Colab Secrets\")\n",
+    "except:\n",
+    "    print(\"⚠️ HF_TOKEN not found in Secrets. Please set it manually or train without upload.\")\n",
+    "    os.environ[\"HF_TOKEN\"] = \"\"\n",
+    "    os.environ[\"HF_REPO_ID\"] = \"luohoa97/BitFin\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Data Generation & Training\n",
+    "This will:\n",
+    "1. Fetch 10 years of history for 70 symbols (if not found).\n",
+    "2. Train the 8-layer Transformer using CUDA (GPU).\n",
+    "3. Save performance metrics and the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add root to path so we can import internal scripts\n",
+    "import sys\n",
+    "sys.path.append(os.getcwd())\n",
+    "\n",
+    "from scripts.train_ai_model import train\n",
+    "\n",
+    "# Trigger the training loop\n",
+    "# Note: It will automatically run build_dataset() if data/trading_dataset.pt is missing\n",
+    "train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Results\n",
+    "Check the generated report and verify model stats."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if os.path.exists(\"performance_report.txt\"):\n",
+    "    with open(\"performance_report.txt\", \"r\") as f:\n",
+    "        print(f.read())\n",
+    "else:\n",
+    "    print(\"Training failed to produce a report.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}